{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "GREqJcq4OLO6", "outputId": "511e84c9-0109-411a-b44e-0801766d0fe9", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" ] } ], "source": [ "# uncomment if working in colab\n", "from google.colab import drive\n", "drive.mount('/content/drive')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "ICZaolBLOLO8", "outputId": "c8c38b1c-e0e3-4258-cc8b-d7d684d9d089", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: seqeval in /usr/local/lib/python3.10/dist-packages (1.2.2)\n", 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"Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.12)\n", "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.3)\n", "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.4)\n", "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2023.6.0)\n", "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.105)\n", "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.105)\n", "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.105)\n", "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch) (8.9.2.26)\n", "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.3.1)\n", "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch) (11.0.2.54)\n", "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch) (10.3.2.106)\n", "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch) (11.4.5.107)\n", "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.0.106)\n", "Requirement already satisfied: nvidia-nccl-cu12==2.20.5 in /usr/local/lib/python3.10/dist-packages (from torch) (2.20.5)\n", "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.105)\n", "Requirement already satisfied: triton==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.3.0)\n", "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch) (12.4.127)\n", "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.5)\n", "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n", " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n" ] } ], "source": [ "# uncomment if using colab\n", "!pip install -q -U datasets\n", "!pip install seqeval\n", "!pip install -U torch\n", "!pip install -q -U evaluate\n", "!pip install -q -U git+https://github.com/huggingface/transformers.git\n", "# !pip install -q -U transformers\n", "!pip install -q -U bitsandbytes\n", "!pip install -q -U git+https://github.com/huggingface/peft.git\n", "!pip install -q -U git+https://github.com/huggingface/accelerate.git\n", "# !pip install -q -U accelerate" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "Km86o_uHOLO8" }, "outputs": [], "source": [ "import torch\n", "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextGenerationPipeline\n", "import os\n", "from utils import *" ] }, { "cell_type": "code", "source": [ "import pandas as pd\n", "from datasets import Dataset, DatasetDict, load_dataset" ], "metadata": { "id": "L0zkxHTmOxkJ" }, "execution_count": 3, "outputs": [] }, { "cell_type": "code", "source": [ "import re" ], "metadata": { "id": "uoWkyegpPCOZ" }, "execution_count": 4, "outputs": [] }, { "cell_type": "code", "execution_count": 5, "metadata": { "id": "L0zE4thvOLO9", "outputId": "75716581-4307-4d98-fddf-887bc825e808", "colab": { "base_uri": "https://localhost:8080/", "height": 145, "referenced_widgets": [ "009541513b8e451886782e457d7cd479", "519375173fe64735a4faccf127abcf90", "484017270bb646119bc605fccb88fa4e", "23dc47bf61314784b7ffb42d995bbce9", "7fdc12ed98be4101a7ff8d15272d5033", "683aa055aae842adabca4ee3fa9a16bf", "6ab04465b07747a592cb28f77c6b5e20", "2379403cd2df4ffd9f7d4eecbf571d7e", "382eff5973b942678fde4a9c6e9c8150", "aec5500c07b54365ab252b5746e08127", "86ed1786dfcc49148bc65e88627a34f3", "36b52f9261ae4c589efc8faab5806509", "8c0ca5e87a1243ba94d821d87dfd7cc1", "1d62a5d167c04e839152933688357dbe", "20614482956a4d359d7e6470fe3b5236", "b46a3ac13be24c04a0c47d424e54d256", "94885d557c64483bbcb842c570e380a5", "0ca2c9f7ad19437886ef4a9aa74cf952", "50557c598d9246599d7e894137d2df26", "ab9a60b256554da0a101dc9d541cbb8c", "523cf83bffdc4d8bbe06c9d886e5d884", "0a508a2d17d04b76aa6bad327e25da96", "7fd41498666942149a04c076f61cb6ad", "32cfc02171c1420599bbbeca98ebfc3b", "c72717e7d2714c7aa695cf5b6ba2481c", "45c84a726d5e470babfb03cd5b139de7", "ec182a1a030c46ac87b2fa998ea9d868", "22972fbed4cc4b408be8abe9f9bb2847", "09fc71a94b2f4766a02b5879ba232e95", "da47463752ff4288a73b1802d37941aa", "9e89840302da47e3a383888883bbadea", "5ecf0ca50841453d85a082217c033fae" ] } }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "VBox(children=(HTML(value='
chemotherapy for unresectable disease.\n", "\n", " Example 2:\n", " Input: Patients with any other severe concurrent disease, which in the judgment of the investigator, would make the patient inappropriate for entry into this study.\n", " Ouput: Patients with any other severe concurrent disease, which in the judgment of the investigator, would make the patient inappropriate for entry into this study.\n", "\n", " As you can see, in each example, the extracted entities are enclosed using the sintax: text of the entity.\n", "\n", " Please now annotate as explained before the following sentence:\n", "\n", " Input: self - reported healthy adults between the ages of 18 - 60 who are fluent in English .\n" ] } ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "id": "QwPSO9NvOLO_", "outputId": "b9b7841d-781a-47bf-92f0-a32c080343d1", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 1307/1307 [15:16:55<00:00, 42.09s/it]\n" ] } ], "source": [ "generated_sentences_p2 = []\n", "for sentence in tqdm(test_dataset_p2['prompt']):\n", " sentence += '\\noutput: '\n", " output = pipe(sentence, max_new_tokens = 500, return_full_text = False, handle_long_generation = \"hole\")[0]['generated_text']\n", " output = output.split('\\n')[0]\n", " generated_sentences_p2.append(output)" ] }, { "cell_type": "code", "source": [ "len(generated_sentences_p2)" ], "metadata": { "id": "uTSOYC9UhKxw", "outputId": "3799dd2c-4f62-4856-a407-0825469f89d4", "colab": { "base_uri": "https://localhost:8080/" } }, "execution_count": 89, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "1307" ] }, "metadata": {}, "execution_count": 89 } ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "id": "x0L_xzJXOLO_" }, "outputs": [], "source": [ "tokenizer = AutoTokenizer.from_pretrained('xlm-roberta-base')" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "id": "6i9B0a6_OLO_" }, "outputs": [], "source": [ "# tokenize and align the labels in the dataset\n", "def tokenize_and_align_labels(sentence, flag = 'I'):\n", " \"\"\"\n", " Tokenize the sentence and align the labels\n", " inputs:\n", " sentence: dict, the sentence from the dataset\n", " flag: str, the flag to indicate how to deal with the labels for subwords\n", " - 'I': use the label of the first subword for all subwords but as intermediate (I-ENT)\n", " - 'B': use the label of the first subword for all subwords as beginning (B-ENT)\n", " - None: use -100 for subwords\n", " outputs:\n", " tokenized_sentence: dict, the tokenized sentence now with a field for the labels\n", " \"\"\"\n", " tokenized_sentence = tokenizer(sentence['tokens'], is_split_into_words=True, truncation=True)\n", "\n", " labels = []\n", " for i, labels_s in enumerate(sentence['ner_tags']):\n", " word_ids = tokenized_sentence.word_ids(batch_index=i)\n", " previous_word_idx = None\n", " label_ids = []\n", " for word_idx in word_ids:\n", " # if the word_idx is None, assign -100\n", " if word_idx is None:\n", " label_ids.append(-100)\n", " # if it is a new word, assign the corresponding label\n", " elif word_idx != previous_word_idx:\n", " label_ids.append(labels_s[word_idx])\n", " # if it is the same word, check the flag to assign\n", " else:\n", " if flag == 'I':\n", " if entities_list[labels_s[word_idx]].startswith('I'):\n", " label_ids.append(labels_s[word_idx])\n", " else:\n", " label_ids.append(labels_s[word_idx] + 1)\n", " elif flag == 'B':\n", " label_ids.append(labels_s[word_idx])\n", " elif flag == None:\n", " label_ids.append(-100)\n", " previous_word_idx = word_idx\n", " labels.append(label_ids)\n", " tokenized_sentence['labels'] = labels\n", " return tokenized_sentence" ] }, { "cell_type": "markdown", "metadata": { "id": "ZpemgZm_OLPA" }, "source": [ "**Standarizing true annotations**" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "id": "-bbV-kgyOLPB" }, "outputs": [], "source": [ "def parse_ann2bio(sentence, pattern, pattern1, pattern2):\n", " # if sentence[-1] == \"\\n\":\n", " # sentence = sentence[:-2] # remove the \\n and a final point wrongly added\n", " # else:\n", " # sentence = sentence[:-1] # remove the final point wrongly added\n", "\n", " # find the entities\n", " occurrences = re.finditer(pattern, sentence)\n", " indexes = [(match.start(), match.end()) for match in occurrences]\n", "\n", " annotation = []\n", " i = 0\n", "\n", "\n", " # create the bio list\n", " for beg, end in indexes:\n", " if beg > i:\n", " annotation.extend([(word, \"O\") for word in sentence[i:beg].split()])\n", " entity = sentence[beg:end]\n", " entity_name = re.search(pattern1, entity).group(1)\n", " entity = entity.replace(f'<{entity_name}>', \"\").replace(f'', \"\")\n", " split_entity = entity.split()\n", " annotation.append((split_entity[0], \"B-\" + entity_name))\n", " annotation.extend([(word, \"I-\" + entity_name) for word in split_entity[1:]])\n", " i = end\n", " annotation.extend([(word, \"O\") for word in sentence[i:].split()])\n", "\n", " # check punctuation sign in tokens and put them as individual tokens\n", " ps = r'(\\.|\\,|\\:|\\;|\\!|\\?|\\-|\\(|\\)|\\[|\\]|\\{|\\}|\\\")'\n", " new_annotation = []\n", " for i,(word, tag) in enumerate(annotation):\n", " if re.search(ps, word):\n", " # find the ocurrences of the punctuation signs\n", " occurrences = re.finditer(ps, word)\n", " indexes = [(match.start(), match.end()) for match in occurrences]\n", " # create the new tokens\n", " last = 0\n", " for j, (beg, end) in enumerate(indexes):\n", " if beg > last:\n", " new_annotation.append((word[last:beg], tag))\n", " if tag != \"O\":\n", " label = f'I-{tag.split(\"-\")[1]}'\n", " else:\n", " label = \"O\"\n", " if end < len(word) or (i < len(annotation) - 1 and annotation[i+1][1] == label):\n", " new_annotation.append((word[beg:end], label))\n", " else:\n", " new_annotation.append((word[beg:end], 'O'))\n", " last = end\n", " if last < len(word):\n", " new_annotation.append((word[last:], label))\n", "\n", " else:\n", " new_annotation.append((word, tag))\n", "\n", "\n", " return new_annotation" ] }, { "cell_type": "code", "source": [ "new_true_annotations = []\n", "for sent in dataset['test']:\n", " annotation = []\n", " for word, tag in zip(sent['tokens'], sent['ner_tags']):\n", " annotation.append((word, entities_list[tag]))\n", " new_annotation = []\n", " ps = r'(\\.|\\,|\\:|\\;|\\!|\\?|\\-|\\(|\\)|\\[|\\]|\\{|\\}|\\\")'\n", " for i,(word, tag) in enumerate(annotation):\n", " if re.search(ps, word):\n", " # find the ocurrences of the punctuation signs\n", " occurrences = re.finditer(ps, word)\n", " indexes = [(match.start(), match.end()) for match in occurrences]\n", " # create the new tokens\n", " last = 0\n", " for j, (beg, end) in enumerate(indexes):\n", " if beg > last:\n", " new_annotation.append((word[last:beg], tag))\n", " if tag != \"O\":\n", " label = f'I-{tag.split(\"-\")[1]}'\n", " else:\n", " label = \"O\"\n", " if end < len(word) or (i < len(annotation) - 1 and annotation[i+1][1] == label):\n", " new_annotation.append((word[beg:end], label))\n", " else:\n", " new_annotation.append((word[beg:end], 'O'))\n", " last = end\n", " if last < len(word):\n", " new_annotation.append((word[last:], label))\n", " else:\n", " new_annotation.append((word, tag))\n", " new_true_annotations.append(new_annotation)\n", "len(new_true_annotations)" ], "metadata": { "id": "Kgi4KQythezT", "outputId": "51229f7b-ae1e-4b5c-d1dc-b477a365ab45", "colab": { "base_uri": "https://localhost:8080/" } }, "execution_count": 93, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "1307" ] }, "metadata": {}, "execution_count": 93 } ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "id": "ng4AD9cxOLPB", "outputId": "3c9b6d49-4b24-4377-8017-4dbc7aba9a3f", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "1307" ] }, "metadata": {}, "execution_count": 94 } ], "source": [ "true_annotations = []\n", "for sent in new_true_annotations:\n", " dicc_sent = {\"tokens\":[], \"ner_tags\":[]}\n", " for word, tag in sent:\n", " dicc_sent[\"tokens\"].append(word)\n", " dicc_sent[\"ner_tags\"].append(sel_ent[tag])\n", " true_annotations.append(dicc_sent)\n", "len(true_annotations)" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "id": "T3gzF1mvOLPB" }, "outputs": [], "source": [ "true_df = pd.DataFrame(true_annotations)\n", "true_ann_dataset = Dataset.from_pandas(true_df)" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "id": "gtFN5ix8OLPB", "outputId": "e2ccd19f-b58b-4a4a-c7e1-c3f118cda808", "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "4a35141fd2544fd08d1c5748a16c28f6", "d6e6a89659834daa88bcef28c1d5d220", "b4f30646efe04849afd1b45f50dd3931", "9a90159ea82b4c3f921839ea7b6f9c86", "8698beb6d78342caaa49ea61c9ebcc58", "5562bbdb7f064ba4867374297f2c7ef4", "f5f58531d6474340a7e5d4ccdc171969", "57e9591c1f1a4be58f8a826059e27d05", "e52d41b0f7084b83abfc2dce289e9757", "49662c4803d846d6bb15e8ba44e4b818", "a09956aac8fb4447b241f1dc646c6429" ] } }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Map: 0%| | 0/1307 [00:00'\n", "pattern2 = r''\n", "pattern = f'{pattern1}.*?{pattern2}'" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "id": "NF6iDWPeOLPC", "outputId": "cad977da-3c36-43ec-9bfa-482971505810", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "1307" ] }, "metadata": {}, "execution_count": 100 } ], "source": [ "new_p2_annotations = []\n", "for sent in generated_sentences_p2:\n", " annotation = parse_ann2bio(sent, pattern, pattern1, pattern2)\n", " new_p2_annotations.append(annotation)\n", "len(new_p2_annotations)" ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "id": "kRxCaGoFOLPC", "outputId": "6e580bb9-a3ca-40d6-8d23-3414e2da292f", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "1307" ] }, "metadata": {}, "execution_count": 101 } ], "source": [ "p2_annotations = []\n", "for sent in new_p2_annotations:\n", " dicc_sent = {\"tokens\":[], \"ner_tags\":[]}\n", " for word, tag in sent:\n", " dicc_sent[\"tokens\"].append(word)\n", " dicc_sent[\"ner_tags\"].append(sel_ent[tag])\n", " p2_annotations.append(dicc_sent)\n", "len(p2_annotations)" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "id": "JJapA5HhOLPC" }, "outputs": [], "source": [ "p2_df = pd.DataFrame(p2_annotations)\n", "p2_dataset = Dataset.from_pandas(p2_df)" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "id": "Wh2C3LSmOLPC", "outputId": "ebd4d143-619c-44f1-e84e-8a06cc2d0ef0", "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "162bfe1f36444ce1861ceaf2c8f6a3f3", "d33896d2c9c546e1b9d5cc12bb866d51", "af607d56eb2c4de88e84315ebf4e5bed", "0f469022363b4b0d96762dc3aafebffe", "dc63518146884067aaebf37d682e83b5", "676f0e00bfba466ea183f41ba064ff95", "91796acd865b49e39500cbc7fdfa2e1e", "48df188dce924c32b510d1ceb4d2450c", "78810abaada343a998519b4cb128bf3a", "5f6c93a23a55413f86aa4778e7d2a6c3", "9b21d0ab5e874bf3aa723bb2114eef80" ] } }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Map: 0%| | 0/1307 [00:00
Copy a token from your Hugging Face\ntokens page and paste it below.
Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file.
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